Adaptive Influence-Based Borrowing for Hybrid Control Trials R package InfluenceBorrowing version 0.1.0 Implements the adaptive influence-based borrowing framework proposed by Qinwei Yang, Jingyi Li, Peng Wu, and Shu Yang (2026+) in the paper ``Improving Treatment Effect Estimation in Trials through Adaptive Borrowing of External Controls" <doi:10.48550/arXiv.2604.13973> for augmenting Randomized Controlled Trials (RCTs) with External Control (EC) data. This package provides a comprehensive workflow to: (1) quantify the comparability of external control samples using influence scores approximated via the influence function of the M-estimator; (2) construct candidate borrowing subsets and select the optimal subset that minimizes the Mean Squared Error (MSE); and (3) calibrate systematic differences in external outcomes using R-learner methods implemented via Ordinary Least Squares or Kernel Ridge Regression. Software Imports: KRLS, stats Jile Chaoge <chogjill@126.com> Comprehensive R Archive Network (CRAN) Jile Chaoge [aut, cre], Peng Wu [aut], Shu Yang [aut] GPL-3 2026-04-23 application/tgz https://CRAN.R-project.org/package=InfluenceBorrowing doi:10.32614/CRAN.package.InfluenceBorrowing